Project Part 2

Group 9

Authors

Affiliation

Thimoté Dupuch

University of Twente

Joris van Lierop

University of Twente

Jurre van Sijpveld

University of Twente

Loading libraries

library(dplyr)
library(forcats)
library(ggplot2)
library(plotly)
library(broom)
library(boot)
library(caret)
library(pROC)

Loading dataset

dataset <- read.csv("SMARTc.csv", sep = ";") # Without missing values

Re-encode the categorical variables

dataset <- mutate(dataset,
    EVENT = factor(EVENT),
    EVENT = fct_recode(EVENT, "no" = "0", "yes" = "1"),
    SEX = factor(SEX),
    SEX = fct_recode(SEX, "male" = "1", "female" = "2"),
    DIABETES = factor(DIABETES),
    DIABETES = fct_recode(DIABETES, "no" = "0", "yes" = "1"),
    SMOKING = factor(SMOKING),
    SMOKING = fct_recode(SMOKING, "never" = "1", "former" = "2", "current" = "3"),
    alcohol = factor(alcohol),
    alcohol = fct_recode(alcohol, "never" = "1", "former" = "2", "current" = "3"),
    CEREBRAL = factor(CEREBRAL),
    CEREBRAL = fct_recode(CEREBRAL, "no" = "0", "yes" = "1"),
    CARDIAC = factor(CARDIAC),
    CARDIAC = fct_recode(CARDIAC, "no" = "0", "yes" = "1"),
    AAA = factor(AAA),
    AAA = fct_recode(AAA, "no" = "0", "yes" = "1"),
    PERIPH = factor(PERIPH),
    PERIPH = fct_recode(PERIPH, "no" = "0", "yes" = "1"),
    albumin = factor(albumin),
    albumin = fct_recode(albumin, "no" = "1", "micro" = "2", "macro" = "3"),
    STENOSIS = factor(STENOSIS),
    STENOSIS = fct_recode(STENOSIS, "no" = "0", "yes" = "1"),
)

Logistic regression model of EVENT

Accessing performance using cross-validation

k <- 5
folds <- createFolds(dataset$EVENT, k = k, list = TRUE, returnTrain = FALSE)
roc_list <- list()

for (i in 1:k) {
    train <- dataset[-folds[[i]], ]
    test <- dataset[folds[[i]], ]

    fit_train <- glm(EVENT ~ AGE + SEX + BMI + SYSTH + HDL + DIABETES +
        HISTCAR2 + HOMOC + log(CREAT) + STENOSIS + IMT + SMOKING +
        alcohol + albumin, data = train, family = "binomial")

    predict_test <- predict(fit_train, newdata = test, type = "response")
    roc_i <- roc(test$EVENT, predict_test)
    roc_list[[i]] <- roc_i
}

roc_df <- do.call(rbind, lapply(1:length(roc_list), function(i) {
    data.frame(
        Fold = paste("Fold", i),
        Sensitivity = roc_list[[i]]$sensitivities,
        Specificity = 1 - roc_list[[i]]$specificities
    )
}))

roc_plot <- ggplot(roc_df, aes(x = Specificity, y = Sensitivity, color = Fold)) +
    geom_line(linewidth = 0.8) +
    scale_color_brewer(palette = "Set1") +
    theme_minimal(base_size = 14) +
    labs(
        title = "ROC Curves for Each Fold",
        x = "1 - Specificity",
        y = "Sensitivity",
        color = "Fold"
    ) +
    theme(
        plot.title = element_text(hjust = 0.5, size = 15, face = "bold"),
        legend.position = "bottom"
    ) +
    coord_equal()

ggplotly(roc_plot)